import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco
import os


class SSD(nn.Module):
    """Single Shot Multibox Architecture
    The network is composed of a base VGG network followed by the
    added multibox conv layers.  Each multibox layer branches into
        1) conv2d for class conf scores
        2) conv2d for localization predictions
        3) associated priorbox layer to produce default bounding
           boxes specific to the layer's feature map size.
    See: https://arxiv.org/pdf/1512.02325.pdf for more details.

    Args:
        phase: (string) Can be "test" or "train"
        size: input image size
        base: VGG16 layers for input, size of either 300 or 500
        extras: extra layers that feed to multibox loc and conf layers
        head: "multibox head" consists of loc and conf conv layers
    """

    def __init__(self, phase, size, base, extras, head, num_classes):
        super(SSD, self).__init__()
        self.phase = phase # phase为train/test
        self.num_classes = num_classes # object的类别数
        self.cfg = (coco, voc)[num_classes == 21] # configuration， voc为21类，coco为201类
                                                  # 因此当class为21时，选择voc的配置
        self.priorbox = PriorBox(self.cfg) # 根据configuration来设置先验框
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size # 输入图像的大小，在改程序中图像大小应为300

        # SSD network
        self.vgg = nn.ModuleList(base) # base为基础的vgg网络
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras) # 论文中新加入的层

        self.loc = nn.ModuleList(head[0]) #head为输出localization与confidence的卷积层
        self.conf = nn.ModuleList(head[1])
        

        ### Non maximum suppression 在这里 layer/functions/detection.py
        if phase == 'test':
            self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)

    def forward(self, x):
        """Applies network layers and ops on input image(s) x.

        Args:
            x: input image or batch of images. Shape: [batch,3,300,300].

        Return:
            Depending on phase:
            test:
                Variable(tensor) of output class label predictions,
                confidence score, and corresponding location predictions for
                each object detected. Shape: [batch,topk,7]

            train:
                list of concat outputs from:
                    1: confidence layers, Shape: [batch*num_priors,num_classes]
                    2: localization layers, Shape: [batch,num_priors*4]
                    3: priorbox layers, Shape: [2,num_priors*4]
        """
        sources = list() # source储存的是六个用于检测的feature map
                         # conv4_3, conv7, conv8_2, conv9_2, conv10_2, conv11_2
        loc = list()
        conf = list()

        # apply vgg up to conv4_3 relu

        # vgg[0-22]的输出为conv4_3 relu
        for k in range(23):
            x = self.vgg[k](x)

        s = self.L2Norm(x)
        sources.append(s)

        # apply vgg up to fc7
        # vgg网络后面添加的部分，即论文中的conv6, conv7
        for k in range(23, len(self.vgg)):
            x = self.vgg[k](x)
        # conv7存储在source中，作为后续要使用的feature map
        sources.append(x)

        # apply extra layers and cache source layer outputs
        # 论文中的Extra Feature Layers, conv8_1, conv8_2,
        # conv9_1, conv9_2, conv10_1, conv10_2, conv11_1, conv11_2
        # 其中
        for k, v in enumerate(self.extras):
            x = F.relu(v(x), inplace=True)
            if k % 2 == 1:
                ### conv8_2, conv9_2, conv10_2, conv11_2加入到source中，
                ### 作为后续要使用的feature map
                sources.append(x)

        # apply multibox head to source layers

        # 将sources中feature map对应的localization与confidence加入到loc, conf列表中
        
        for (x, l, c) in zip(sources, self.loc, self.conf):
            loc.append(l(x).permute(0, 2, 3, 1).contiguous())
            conf.append(c(x).permute(0, 2, 3, 1).contiguous())

        # 将loc, conf列表中的值串在一起
        loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
        conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)

        
        # Test模式时，需要有NMS.
        """At test time, Detect is the final layer of SSD.  Decode location preds,
        apply non-maximum suppression to location predictions based on conf
        scores and threshold to a top_k number of output predictions for both
        confidence score and locations.
        """
        # 上面对detect的初始化为：self.detect = Detect(num_classes, bkg_label=0, 200, 0.01, 0.45)
        # num_classes : 类别数目
        # 0: background label， 背景的label为0.
        # 200: top-k. 过滤到只剩top-k个预测框。
        # 0.01: confidence threshold
        # 0.45: 做NMS时的threshold

        # Detect的Forward为：

        # def forward(self, loc_data, conf_data, prior_data):
        """
        Args:
            loc_data: (tensor) Loc preds from loc layers
                Shape: [batch,num_priors*4]
            conf_data: (tensor) Shape: Conf preds from conf layers
                Shape: [batch*num_priors,num_classes]
            prior_data: (tensor) Prior boxes and variances from priorbox layers
                Shape: [1,num_priors,4]
        """
        if self.phase == "test":
            output = self.detect(
                loc.view(loc.size(0), -1, 4),                   # loc preds
                self.softmax(conf.view(conf.size(0), -1,
                             self.num_classes)),                # conf preds
                self.priors.type(type(x.data))                  # default boxes
            )


        else:
            output = (
                loc.view(loc.size(0), -1, 4), # 展成为4的数值
                conf.view(conf.size(0), -1, self.num_classes), # 展成为num_classes的数值
                self.priors
            )
        return output

    def load_weights(self, base_file):
        other, ext = os.path.splitext(base_file)
        if ext == '.pkl' or '.pth':
            print('Loading weights into state dict...')
            self.load_state_dict(torch.load(base_file,
                                 map_location=lambda storage, loc: storage))
            print('Finished!')
        else:
            print('Sorry only .pth and .pkl files supported.')


# This function is derived from torchvision VGG make_layers()
# https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
def vgg(cfg, i, batch_norm=False):
    layers = []
    in_channels = i

    #此时的cfg为[64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
    #       512, 512, 512]
    #       i = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif v == 'C':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [pool5, conv6,
               nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
    return layers


def add_extras(cfg, i, batch_norm=False):
    # Extra layers added to VGG for feature scaling

    # 此时的cfg为[256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256]
    # i = 1024
    layers = []
    in_channels = i
    flag = False
    for k, v in enumerate(cfg):
        if in_channels != 'S':
            if v == 'S':
                layers += [nn.Conv2d(in_channels, cfg[k + 1],
                           kernel_size=(1, 3)[flag], stride=2, padding=1)]
            else:
                layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
            flag = not flag
        in_channels = v
    return layers


def multibox(vgg, extra_layers, cfg, num_classes):

    # multibox的输入为之前的vgg网络以及extra_layers网络
    # 此时的cfg为[4, 6, 6, 6, 4, 4]
    loc_layers = []
    conf_layers = []
    vgg_source = [21, -2]

    # loc_layers为conv4_3, conv7, conv8_2, conv9_2, conv10_2, conv11_2
    # 对应的接下来的生成localization数值的卷积层
    # conf_layers为这些特征图对应的接下来生成confidence数值的卷积层
    for k, v in enumerate(vgg_source):
        loc_layers += [nn.Conv2d(vgg[v].out_channels,
                                 cfg[k] * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(vgg[v].out_channels,
                        cfg[k] * num_classes, kernel_size=3, padding=1)]
    for k, v in enumerate(extra_layers[1::2], 2):
        loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                 * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                  * num_classes, kernel_size=3, padding=1)]
    return vgg, extra_layers, (loc_layers, conf_layers)


base = {
    '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
            512, 512, 512],
    '512': [],
}
extras = {
    '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
    '512': [],
}
mbox = {
    '300': [4, 6, 6, 6, 4, 4],  # number of boxes per feature map location
    '512': [],
}


def build_ssd(phase, size=300, num_classes=21):
    if phase != "test" and phase != "train":
        print("ERROR: Phase: " + phase + " not recognized")
        return
    if size != 300:
        print("ERROR: You specified size " + repr(size) + ". However, " +
              "currently only SSD300 (size=300) is supported!")
        return
    base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
                                     add_extras(extras[str(size)], 1024),
                                     mbox[str(size)], num_classes)
    return SSD(phase, size, base_, extras_, head_, num_classes)
